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We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
One is focused on second-order spatial information to increase the performance of image descriptors, both local and global.
In this paper, we propose to learn high per- formance descriptor in Euclidean space via the Convolu- tional Neural Network (CNN).
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on Face Sketch Synthesis on CUHK
In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
Ranked #5 on Multi-tissue Nucleus Segmentation on Kumar
Recent works show that local descriptor learning benefits from the use of L2 normalisation, however, an in-depth analysis of this effect lacks in the literature.
We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches.